Amazon2M Dataset.
For the Amazon2M dataset, we follow the experimental settings of [1]}.
As our baseline, we adopt two, three, and four-layer GraphSAGE models [2]} with hidden dimensions of 400 and 4096 (we refer to these models as “narrow” and “wide”, respectively).
The training graph is partitioned into \(15,\!000\)  sub-graphs and a batch size of 10 is used.
Models are trained for 400 total epochs with the Adam optimizer [3]} and no weight decay.
Performance is measured in terms of F1 score and training time.
For all experiments with GIST, \(\zeta = 5000\)  local iterations are used, which we find to perform consistently well.
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